AI ENGINEER JOB DESCRIPTION

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AI Engineers design, build, integrate and improve artificial intelligence systems that solve real business problems.

They work across machine learning, software engineering, data, cloud platforms and AI-enabled applications, helping organizations move from experimentation to production-ready AI systems. Depending on the role, an AI Engineer may work with LLMs, GenAI applications, model integration, APIs, MLOps, automation, data pipelines or AI product development.

This AI Engineer job description guide is designed to help employers define the role clearly and help candidates understand the responsibilities, skills and experience commonly expected in AI engineering jobs.

An AI Engineer is responsible for building systems and applications that use artificial intelligence to improve products, processes or decision-making.

This can include integrating machine learning models into applications, building LLM-powered tools, designing AI workflows, connecting models to data sources, developing APIs, testing model outputs and helping AI systems work reliably in production.

AI Engineers often work closely with data scientists, machine learning engineers, software engineers, data engineers, product managers, cloud teams and business stakeholders.



The exact responsibilities can vary depending on the organization’s AI maturity. Some AI Engineers focus heavily on LLM and GenAI applications. Others work closer to machine learning engineering, MLOps, data engineering, automation, platform engineering or AI product development.

An AI Engineer may be responsible for:



• designing, building and improving AI-enabled applications
• integrating AI and machine learning models into products or workflows
• building APIs and services that connect AI models to business systems
• working with large language models, GenAI tools and AI orchestration frameworks
• developing retrieval-augmented generation systems
• creating prompts, evaluation frameworks and model testing processes
• supporting model deployment, monitoring and performance improvement
• working with data pipelines, structured data and unstructured data
• collaborating with data scientists, machine learning engineers and software engineers
• improving reliability, scalability and maintainability of AI systems
• supporting responsible AI, governance, privacy and risk requirements
• experimenting with new AI tools, frameworks and techniques
• documenting technical decisions, model behavior and system design
• translating business problems into practical AI solutions
• troubleshooting issues with model outputs, performance or integrations

A strong AI Engineer usually combines software engineering, AI knowledge, data understanding and practical problem-solving.

Common AI Engineer skills include:

• Python
• software engineering
• APIs and system integration
• machine learning
• LLMs and GenAI
• prompt engineering
• retrieval-augmented generation
• vector databases
• model evaluation
• data pipelines
• cloud platforms
• MLOps
• Docker and Kubernetes
• CI/CD
• testing and monitoring
• data security and privacy awareness
• responsible AI understanding
• communication
• problem-solving
• product thinking

The exact skill set will depend on the role. An LLM-focused AI Engineer may need more experience with prompt engineering, embeddings, RAG and model evaluation. A production-focused AI Engineer may need stronger MLOps, cloud, APIs, monitoring and software engineering experience.

AI Engineers may work with a wide range of tools depending on the organization’s AI environment.

Common tools and technologies may include:

• Programming and engineering: Python, JavaScript, TypeScript, Java, Git, APIs
• AI and ML frameworks: PyTorch, TensorFlow, scikit-learn, Keras
• LLM and GenAI tools: OpenAI, Anthropic, Hugging Face, LangChain, LlamaIndex
• Vector databases and search: Pinecone, Weaviate, Chroma, FAISS, Elasticsearch
• Cloud platforms: AWS, Azure, Google Cloud
• Data platforms: Databricks, Snowflake, BigQuery, Redshift, Synapse
• MLOps and deployment: Docker, Kubernetes, MLflow, Kubeflow, CI/CD
• Monitoring and observability: model monitoring, logging, testing and evaluation tools
• Data processing: Spark, Kafka, Airflow
• Application development: APIs, microservices, serverless architecture and product integrations



Employers should avoid turning the job description into a long list of every AI tool available. It is usually better to separate essential engineering skills from desirable platform or framework experience.

AI Engineer Job Description Template

We are looking for an AI Engineer to design, build and improve AI-enabled applications and systems.

The successful candidate will work with product, data, engineering and business teams to develop practical AI solutions that support real business needs.

You will be responsible for integrating AI models into applications, building reliable AI workflows, working with data sources, testing model outputs and supporting production-ready AI systems.

Key Responsibilities

Design, build and improve AI-enabled applications and workflows.

Integrate AI and machine learning models into products, services or business systems.

Build APIs and services that connect AI tools with internal platforms.

Work with LLMs, GenAI tools, embeddings and retrieval-augmented generation.

Develop prompts, evaluation methods and testing processes.

Support model deployment, monitoring and performance improvement.

Work with structured and unstructured data sources.

Collaborate with data scientists, machine learning engineers, software engineers and product teams.

Improve the scalability, reliability and maintainability of AI systems.

Document system design, technical decisions and model behavior.

Support responsible AI, privacy, security and governance requirements.

Required Skills and Experience

Experience in AI engineering, machine learning engineering, software engineering or applied AI.

Strong Python skills.

Experience building APIs, services or application integrations.

Understanding of machine learning, LLMs or GenAI systems.

Experience working with cloud platforms such as AWS, Azure or Google Cloud.

Experience working with data pipelines, databases or data platforms.

Strong software engineering and problem-solving skills.

Ability to test, evaluate and improve AI outputs.

Strong communication and collaboration skills.

Desirable Skills

Experience with OpenAI, Anthropic, Hugging Face, LangChain or LlamaIndex.

Experience with RAG, embeddings, vector databases or semantic search.

Experience with PyTorch, TensorFlow or scikit-learn.

Experience with Docker, Kubernetes, CI/CD or MLOps tools.

Experience with Databricks, Snowflake, BigQuery or similar data platforms.

Knowledge of model monitoring, observability and evaluation frameworks.

Understanding of responsible AI, explainability, privacy and governance.

Experience taking AI systems from prototype to production. 

AI Engineer compensation in the US can vary significantly depending on location, seniority, technical depth, industry, working model and whether the role is permanent or contract.

Employers should benchmark compensation before going to market, especially for roles requiring LLM engineering, GenAI, MLOps, cloud platforms, production AI experience, model evaluation or senior technical leadership.

Candidates will often consider the full package, not just base salary. Remote flexibility, bonus, equity, healthcare benefits, technical ownership, access to quality data, product impact, responsible AI maturity and career progression can all influence whether a role is attractive.



KDR can support employers with compensation benchmarking and market insight before a search begins.

Hiring an AI Engineer starts with understanding what kind of AI problem the organization needs to solve.

Before going to market, employers should be clear on:

whether the role is focused on LLMs, GenAI, machine learning, MLOps, software engineering, automation or AI product development

what AI systems, products or workflows the person will build

whether the work is experimental, production-focused or a blend of both

which tools and platforms are essential

what data environment the person will work with

what level of software engineering experience is required

what level of ownership the person will have

what compensation range is realistic

how the hiring process will assess practical AI engineering ability

KDR helps US organizations hire AI Engineers across permanent, contract and senior roles. We can support role definition, candidate search, shortlist creation, interview coordination, compensation advice and offer management.

AI Engineers can move in several directions as their experience grows.

Some progress into Senior AI Engineer, Lead AI Engineer, AI Architect, Head of AI Engineering or Head of AI roles. Others move toward machine learning engineering, MLOps, AI platform engineering, software architecture, data science or AI product leadership.

A typical career path may include:

• Software Engineer, Data Scientist, Machine Learning Engineer or Data Engineer
• AI Engineer
• Senior AI Engineer
• Lead AI Engineer
• AI Architect
• Head of AI Engineering
• Head of AI
• Director of AI
• Chief AI Officer



Career progression often depends on technical depth, software engineering quality, production AI experience, stakeholder influence, architecture ownership and the ability to connect AI delivery to business outcomes.